Lecture-based teaching paired with laboratory-based exercises is most commonly used in cybersecurity instruction. However, it focuses more on theories and models but fails to provide learners with practical problem-solving skills and opportunities to explore real-world cybersecurity challenges. Problem-based Learning (PBL) has been identified as an efficient pedagogy for many disciplines, especially engineering education. It provides learners with real-world complex problem scenarios, which encourages learners to collaborate with classmates, ask questions and develop a deeper understanding of the concepts while solving real-world cybersecurity problems. This paper describes the application of the PBL methodology to enhance professional training-based cybersecurity education. The authors developed an online laboratory environment to apply PBL with Knowledge-Graph (KG) based guidance for hands-on labs in cybersecurity training.Learners are provided access to a virtual lab environment with knowledge graph guidance to simulated real-life cybersecurity scenarios. Thus, they are forced to think independently and apply their knowledge to create cyber-attacks and defend approaches to solve problems provided to them in each lab. Our experimental study shows that learners tend to gain more enhanced learning outcomes by leveraging PBL with knowledge graph guidance, become more aware of cybersecurity and relevant concepts, and also express interest in keep learning of cybersecurity using our system.
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Knowledge Graph based Learning Guidance for Cybersecurity Hands-on Labs
Hands-on practice is a critical component of cybersecurity education.
Most of the existing hands-on exercises or labs materials
are usually managed in a problem-centric fashion, while it lacks a
coherent way to manage existing labs and provide productive lab
exercising plans for cybersecurity learners. With the advantages
of big data and natural language processing (NLP) technologies,
constructing a large knowledge graph and mining concepts from unstructured
text becomes possible, which motivated us to construct
a machine learning based lab exercising plan for cybersecurity education.
In the research presented by this paper, we have constructed
a knowledge graph in the cybersecurity domain using NLP technologies
including machine learning based word embedding and
hyperlink-based concept mining. We then utilized the knowledge
graph during the regular learning process based on the following
approaches: 1. We constructed a web-based front-end to visualize
the knowledge graph, which allows students to browse and search
cybersecurity-related concepts and the corresponding interdependence
relations; 2. We created a personalized knowledge graph for
each student based on their learning progress and status; 3.We built
a personalized lab recommendation system by suggesting more relevant
labs based on students’ past learning history to maximize their
learning outcomes. To measure the effectiveness of the proposed
solution, we have conducted a use case study and collected survey
data from a graduate-level cybersecurity class. Our study shows
that, by leveraging the knowledge graph for the cybersecurity area
study, students tend to benefit more and show more interests in
cybersecurity area.
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- Award ID(s):
- 1723440
- PAR ID:
- 10193689
- Date Published:
- Journal Name:
- ACM Global Computing Education Conference (CompEd)
- Page Range / eLocation ID:
- 194 to 200
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3300115.3309531
